In the multi-stage job scenarios, minimizing the average CCT does
not necessarily lead to minimizing jobs completion time because the
dependencies in a job should be considered. To tackle this problem,
several heuristics [1]} and approximation solutions
[2]}, [3]} have recently been proposed to minimize
JCT. However, they simplified the problem with some relaxation and
ignored the workload characteristics. In recent years, with the development
of artificial intelligence, machine learning methods have been increasingly
applied to task scheduling problems in cloud data centers. Reinforcement
learning (RL), as a current research hotspot in the field of machine
learning, is used by many researchers to solve task scheduling problems
in complex data center environments. [4]}, [5]}, [6]}.
RL can learn directly from the actual working environment without
relying on inaccurate empirical summaries. Compared with traditional
reinforcement learning, Deep Reinforcement Learning (DRL) [7]}
has powerful representation capabilities of neural networks and decision-making
capabilities in the field of control.
